This valuable volume offers a systematic approach to flight vehicle system identification and exhaustively covers the time domain methodology. It addresses in detail the theoretical and practical aspects of various parameter estimation methods, including those in the stochastic framework and focusing on nonlinear models, cost functions, optimization methods, and residual analysis. A pragmatic and balanced account of pros and cons in each case is provided. The book also presents data gathering and model validation, and covers both large-scale systems and high-fidelity modeling. Real world problems dealing with a variety of flight vehicle applications are addressed and solutions are provided. Examples encompass such problems as estimation of aerodynamics, stability, and control derivatives from flight data, flight path reconstruction, nonlinearities in control surface effectiveness, stall hysteresis, unstable aircraft, and other critical considerations.

Although many books have been written on the theory of system identification, few are available that provide a complete engineering treatment of system identification and how to successfully apply it to flight vehicles. This book presents proven methods, practical guidelines, and real-world flight-test results for a wide range of state-of-the-art flight vehicles, from small uncrewed aerial vehicles (UAVs) to large manned aircraft/rotorcraft.

This book provides a comprehensive overview of both the theoretical underpinnings and the practical application of aircraft modeling based on experimental data also known as aircraft system identification. Much of the material presented comes from the authors own extensive research and teaching activities at the NASA Langley Research Center, and is based on real-world applications of system identification to aircraft. The book uses actual flight-test and wind-tunnel data for case studies and examples, and is a valuable resource for researchers and practicing engineers, as well as a textbook for postgraduate and senior-level courses. [...] The methods and algorithms explained in the book are implemented in a NASA software toolbox called SIDPAC (System IDentification Programs for AirCraft). SIDPAC is written in MATLAB®, and is available by request from NASA Langley Research Center. SIDPAC is composed of many different tools that implement a wide variety of approaches explained fully in the book. These tools can be readily applied to solve aircraft system identification problems.

Unmanned air vehicles are becoming increasingly popular alternatives for private applications which include, but are not limited to, fire fighting, search and rescue, atmospheric data collection, and crop surveys, to name a few. Among these vehicles are avian-inspired, flapping-wing designs, which are safe to operate near humans and are required to carry payloads while achieving manoeuverability and agility in low speed flight. Conventional methods and tools fall short of achieving the desired performance metrics and requirements of such craft. Flight dynamics and system identification for modern feedback control provides an in-depth study of the difficulties associated with achieving controlled performance in flapping-wing, avian-inspired flight, and a new model paradigm is derived using analytical and experimental methods, with which a controls designer may then apply familiar tools. This title consists of eight chapters and covers flapping-wing aircraft and flight dynamics, before looking at nonlinear, multibody modelling as well as flight testing and instrumentation. Later chapters examine system identification from flight test data, feedback control and linearization.

Robust and Adaptive Control shows the reader how to produce consistent and accurate controllers that operate in the presence of uncertainties and unforeseen events. Driven by aerospace applications the focus of the book is primarily on continuous-dynamical systems. The text is a three-part treatment, beginning with robust and optimal linear control methods and moving on to a self-contained presentation of the design and analysis of model reference adaptive control (MRAC) for nonlinear uncertain dynamical systems. Recent extensions and modifications to MRAC design are included, as are guidelines for combining robust optimal and MRAC controllers. Features of the text include: · case studies that demonstrate the benefits of robust and adaptive control for piloted, autonomous and experimental aerial platforms; · detailed background material for each chapter to motivate theoretical developments; · realistic examples and simulation data illustrating key features of the methods described; and · problem solutions for instructors and MATLAB® code provided electronically. The theoretical content and practical applications reported address real-life aerospace problems, being based on numerous transitions of control-theoretic results into operational systems and airborne vehicles that are drawn from the authors’ extensive professional experience with The Boeing Company. The systems covered are challenging, often open-loop unstable, with uncertainties in their dynamics, and thus requiring both persistently reliable control and the ability to track commands either from a pilot or a guidance computer. Readers are assumed to have a basic understanding of root locus, Bode diagrams, and Nyquist plots, as well as linear algebra, ordinary differential equations, and the use of state-space methods in analysis and modeling of dynamical systems. Robust and Adaptive Control is intended to methodically teach senior undergraduate and graduate students how to construct stable and predictable control algorithms for realistic industrial applications. Practicing engineers and academic researchers will also find the book of great instructional value.